TY - GEN
T1 - Aircraft trajectory prediction and risk assessment using bayesian updating
AU - Wang, Yuhao
AU - Pang, Yutian
AU - Liu, Yongming
AU - Dutta, Parikshit
AU - Yang, Bong Jun
N1 - Funding Information:
The research reported in this paper was supported by funds from NASA University Leadership Initiative program (Contract No. NNX17AJ86A, Project Officer: Dr. Kai Goebel, Principal Investigator: Dr. Yongming Liu). The support is gratefully acknowledged.
Publisher Copyright:
© 2019, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Flight trajectory prediction is crucial in maintaining the safety and predicting accidents in the National Airspace System (NAS). The reported work used Bayesian updating to achieve flight trajectory prediction and real-time risk assessment in the NAS. The trajectory simulation is done using NATS, a novel flights simulation platform. The model can consider multiple sources of uncertainties such as weather, human performance etc. Through Bayesian updating, the uncertainty in the model can be reduced given observable quantities. In this article, the Bayesian framework in updating model parameter through observation is introduced. The NATS simulation for a real accident scenario at SFO airport will be presented. In the presented framework, the risk probability is updated continuously using the aircraft location tracking information. The accident can be predicted well before it happens. A criterion for assessing the risk probability is developed under the NATS platform. The risk probability is evaluated based on the separation between aircrafts. It can work as a computer-aided algorithm for Air Traffic Management (ATM) aiming to help the ATC operator in preventing potential accidents.
AB - Flight trajectory prediction is crucial in maintaining the safety and predicting accidents in the National Airspace System (NAS). The reported work used Bayesian updating to achieve flight trajectory prediction and real-time risk assessment in the NAS. The trajectory simulation is done using NATS, a novel flights simulation platform. The model can consider multiple sources of uncertainties such as weather, human performance etc. Through Bayesian updating, the uncertainty in the model can be reduced given observable quantities. In this article, the Bayesian framework in updating model parameter through observation is introduced. The NATS simulation for a real accident scenario at SFO airport will be presented. In the presented framework, the risk probability is updated continuously using the aircraft location tracking information. The accident can be predicted well before it happens. A criterion for assessing the risk probability is developed under the NATS platform. The risk probability is evaluated based on the separation between aircrafts. It can work as a computer-aided algorithm for Air Traffic Management (ATM) aiming to help the ATC operator in preventing potential accidents.
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U2 - 10.2514/6.2019-2936
DO - 10.2514/6.2019-2936
M3 - Conference contribution
AN - SCOPUS:85083979421
SN - 9781624105890
T3 - AIAA Aviation 2019 Forum
BT - AIAA Aviation 2019 Forum
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - AIAA Aviation 2019 Forum
Y2 - 17 June 2019 through 21 June 2019
ER -